*5.6. Multi-Step Forecasting Capacity Test*

Accurate one-step forecasting enables decision-makers to create proper policies and measures of the power supply before one duration. Multi-step forecasting can provide multi-step future consumption information in advance. To validate the multi-step forecasting capacity of the proposed method for multiple forecasts, we designed a five-step forecasting experiment and compared it to [27], which tests the multi-step forecasting capacity of their model for VSTF. The input of the proposed MCSCNN–LSTM for multi-step forecasting in Figure 2 has the same shape with one-step forecasting, we only need to change the output into one vector with five elements regarding the five future consumption data points of each forecast. The comparative results using averaged RMSE, MAE, and MAPE of 10 times are shown in Table 13. AEP is adopted for VSTF and LTF; COMED and DAYTON are adopted for STF and MTF. The results indicate the proposed MCSCNN–LSTM performs very well

for STF, MTF, and LTF. Especially, the performance of MTF and LTF has increased tenfold compared to the method of [27] using RMSE, MAE, and MAPE. Only the VSTF is a little worse than [27], but they still are the same level. We also give one sample of five-step forecasting of different forecasts as shown in Figure 9. We can see the proposed MCSCNN–LSTM accurately predicts all types of trends from the raw data and it outperforms [27] CNN–LSTM in terms of handing details. Notably, the proposed method has an absolute advantage in terms of STF, MTF, and LTF.

**Figure 8.** The average MAPE of different methods to validate the transfer learning capacity of the proposed MCSCNN–LSTM for different forecasts. (**a**) Transfer learning capacity test for VSTF. (**b**) Transfer learning capacity test for STF. (**c**) Transfer learning capacity test for MTF. (**d**) Transfer learning capacity test for LTF.

**Table 13.** The multi-step forecasting capacity test results.


**Figure 9.** A comparison of the results of the five-step forecasting using the proposed method and CNN–LSTM [27]. The results indicate the proposed method has an absolute advantage in terms of STF, MTF, and LTF. For VSTF, CNN–LSTM performs a little better than proposed MCSCNN–LSTM. (**a**) Five-step electricity forecasting results for VSTF. (**b**) Five-step electricity forecasting results for STF. (**c**) Five-step electricity forecasting results for MTF. (**d**) Five-step electricity forecasting results for LTF.
